Thu Oct 08, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Wang, Xingying, Liu, Shengjie, Hu, Litang, Sun, Jianchong, Zhang, Junchao, and Zhu, Zhenyuan, 2026. Groundwater Storage Dynamics and Attribution in the Wei River Basin Based on Dynamic Downscaling. Remote Sensing, 18(17):3013, doi:10.3390/rs18173013.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2026RemS...18.3013W,
author = {{Wang}, Xingying and {Liu}, Shengjie and {Hu}, Litang and {Sun}, Jianchong and {Zhang}, Junchao and {Zhu}, Zhenyuan},
title = "{Groundwater Storage Dynamics and Attribution in the Wei River Basin Based on Dynamic Downscaling}",
journal = {Remote Sensing},
keywords = {Wei River Basin, GRACE, dynamic downscaling, groundwater storage changes, contribution of human activities, soil erosion correction},
year = 2026,
month = sep,
volume = {18},
number = {17},
eid = {3013},
pages = {3013},
abstract = "{What are the main findings? In this study, a dynamically downscaling
groundwater storage model, specifically corrected for soil
erosion mass loss, was developed and revealed a west-to-east
decreasing gradient in groundwater storage across the Wei River
Basin. Human activities are the primary driver of regional
groundwater dynamics, contributing over 80\% to storage changes
in most years, far exceeding natural climate factors like
precipitation and evapotranspiration. In this study, a
dynamically downscaling groundwater storage model, specifically
corrected for soil erosion mass loss, was developed and revealed
a west-to-east decreasing gradient in groundwater storage across
the Wei River Basin. Human activities are the primary driver of
regional groundwater dynamics, contributing over 80\% to storage
changes in most years, far exceeding natural climate factors
like precipitation and evapotranspiration. What are the
implications of the main findings? Integrating soil erosion
corrections into GRACE data processing provides a critical
methodological reference for accurately isolating groundwater
signals in loess plateaus or other severe erosion-prone basins
globally. The dominance of human-induced depletion and
significant seasonal volatility in the mid-lower reaches
urgently require policymakers to prioritize strict anthropogenic
extraction controls over relying on natural recharge.
Integrating soil erosion corrections into GRACE data processing
provides a critical methodological reference for accurately
isolating groundwater signals in loess plateaus or other severe
erosion-prone basins globally. The dominance of human-induced
depletion and significant seasonal volatility in the mid-lower
reaches urgently require policymakers to prioritize strict
anthropogenic extraction controls over relying on natural
recharge. Intensive groundwater exploitation in the Wei River
Basin (WRB) has caused severe depletion. While Gravity Recovery
and Climate Experiment (GRACE) satellites monitor these changes,
their coarse resolution fails to account for soil erosion-
induced mass loss on the Loess Plateau limit basin-scale
accuracy. To address this, we developed the groundwater storage
model to dynamically downscale GRACE data to the resolution of
0.05{\textdegree} grid. This physically based model integrates
Darcy's law, water-balance principles, and an innovative
correction for soil erosion mass migration. Validated against
well data with maximum correlative coefficient of 0.73, the
model reveals a severe groundwater decline of 55.89 {\texttimes}
{}10$^{8}$ m$^{3}$/year from 2003 to 2023. The high-resolution
results successfully capture localized over-extraction hotspots
in the Guanzhong Plain, showing a west-to-east decreasing
gradient in groundwater storage along the Wei River channel.
Furthermore, quantitative analysis indicates that human
activities, including primarily agricultural and urban
extraction, are the overwhelming drivers, accounting for over
80\% of storage depletion. This framework provides a robust
methodological reference for isolating groundwater signals in
erosion-prone regions and supports refined water management in
semi-arid basins.}",
doi = {10.3390/rs18173013},
adsurl = {https://ui.adsabs.harvard.edu/abs/2026RemS...18.3013W},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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